A Process Model for Evaluating GenAI Adoption and Use in Software Development

GenAI Quality Evaluation in Enterprise Settings: A Research Dataset on Practices, Roles, and Risk Management for Software DevelopmentThis dataset presents empirical research on Generative AI (GenAI) quality evaluation practices within enterprise environments. The dataset comprises multi-source data collected through interviews, archival analysis, and case study verification, providing insights into how organizations approach quality assessment for GenAI systems across different phases of adoption and use. The dataset includes:Interview Data: Thematic analysis of 20 key themes derived from enterprise stakeholder interviews, covering GenAI adoption processes, quality aspects, role responsibilities, evaluation practices, and coordination challengesLegal Risk Framework: Comprehensive assessment of 12 risk categories including intellectual property, data privacy, contractual liability, regulatory compliance, business risks, and technical vulnerabilities, with detailed mitigation strategies and approval requirementsRole Responsibility Matrix: Detailed mapping of 13 professional roles across three phases (Business Idea, Development, Operation) with specific quality responsibilities and collaboration patternsQuality Characteristics Framework: Evaluation of quality characteristics based on ISO\/IEC 25059 standards, covering product quality and quality-in-use dimensions with measurement approachesLessons Learned: Lessons learned from real-world GenAI implementations covering process improvements, monitoring strategies, and organizational learningThe dataset addresses critical gaps in understanding enterprise GenAI quality evaluation by providing structured insights into distributed ownership models, legal compliance challenges, and the evolution from informal assessment practices to structured evaluation models. Key findings reveal the need for cross-functional coordination, specialized GenAI quality metrics, and continuous monitoring approaches that differ from traditional software quality assessments.This dataset is valuable for researchers studying AI governance, software quality engineering, enterprise AI adoption, and organizational change management. It provides empirical evidence for developing better GenAI quality frameworks and supports comparative studies across different organizational contexts.Data FormatCSV files with structured thematic and categorical dataPDF documents with detailed frameworks and case descriptionsMulti-phase data collection spanning business planning through operational deployment Potential ApplicationsDevelopment of GenAI quality evaluation modelsEnterprise AI governance researchSoftware quality engineering methodology developmentOrganizational change management studiesRisk assessment framework validationCross-industry comparative analysis of AI adoption practices